Jie Tan

I joined the Brain team at Google, working on deep learning, reinforcement learning and robotics.

I defended my PhD thesis on October 2015 and joined Lytro. I was a Member of Technical Staff at the Computational Imaging group at Lytro, working on computer vision, light field processing and visual effects.

My research focused on developing computational tools to understand, simulate and control human and animal locomotion in a complex environment. I developed fast and stable computer programs to simulate complex dynamic systems, such as fluid, soft body and articulated rigid bodies. I am interested in applying optimal control and machine learning techniques to enable computers to automatically learn locomotion skills inside a complex physical environment.

I am also interested in transferring the controllers that are learned in simulations to real robots. Controllers learned in a simulation usually perform poorly on real robots due to the discrepancies between the simulated and the real system. I am developing tools to understand and model such discrepancies. I augmented the physical simulation using real-world data, which not only increases the simulation accuracy, but also improves the real-world performance of the controllers.